What is the probit latent class model?
The probit latent class model is a version of LCA based on the following assumptions: • Manifest variables (binary or ordered-categorical; purely nominal variables are excluded) are discretized versions of latent continuous variables. • Discretization occurs as the result of fixed thresholds that divide a latent continuous variable into distinct regions that correspond to observed response levels. • For each latent class, the latent continuous variables have a multivariate-normal distribution. The estimated parameters for the probit latent class model are: (i) the means of each latent continuous variable for each latent distribution (i.e., distribution centroids); (ii) the variance/covariance matrix for latent continuous variables in each latent distribution; (iii) the threshold locations that divide each latent continuous variable into different regions; and (iv) the latent class prevalences. For a basic latent class model, the covariance parameters are assumed equal to 0, which is th
